In addition to the main data view there should

In addition to the main data view there should

Or also called “raw data view”, is a data view where all data is recorded unfiltered. As the name suggests, you use this in addition to data view as a backup if, for some reason, a filter is set incorrectly in the main data view and data gaps may result. This can sometimes happen if you have incorrectly set a filter incorrectly and it excludes more data than desired. The backup view then pops up so that you can see the data for the missing time period. Two examples of faulty filters: Inclusion filter used instead of exclusion filter If you are working with a regular expression and have put a “|” at the end of the expression and it suddenly excludes all data Depending on the area relevant to you, you can create additional data views.

Four factors play a role in core updates

For example, your data view setup might look like this: Example data views in Google Analytics 2. Use filters in the overall data view! The main data view should be the one used for evaluations. Important filters Malaysia Telegram Number Data are set here so as not to “dilute” the collected data with your own access. You can find the filter settings under “Administration” > “Data view” > “Filter”. The following filters should primarily be taken into consideration: IP filter: for your own access or the customer’s. Due to IP anonymization, the last part of the number block is no longer transmitted. The settings are therefore as follows: Example of IP filters in Google Analytics Hostname filter: Inclusion of the domain for which the data is collected.

I suspect the following sources, but of course these are pure

If you use subdomains, please remove the “www” first. Example hostname filter in Google Analytics Further filters such as the exclusion of spam referral sources. It can happen that so-called fake bots or Cambodia Phone Number fake access (usually in the form of referrals) are recorded. Most of the time they come and go quickly, but their legacy remains. Unfortunately, they falsify your data. There’s only one thing that helps: Check your data regularly and react early. Using the example shown, you should at least exclude the first two sources: Examples of spam traffic in Google Analytics For example like this: Example spam filter in Google Analytics And not like this: Example of incorrect spam filter in Google Analytics This bar at the end then ensures that all further access is filtered out. You should definitely pay attention to this. Important: Check and monitor your filters in the real-time report.